
A representation of the human visual system (HVS) is necessary to establish a robust image quality metric which is needed for computer vision applications [1]}, [2]}. Classical approaches considered hand-crafted strategies to mimic the properties of the HVS [3]}, [4]}, [5]}, [6]} by implementing a stream of computational functions that are combined to identify the key perceptual properties of images. While these techniques played their role effectively, scaling up these methods have proven to be a daunting task, especially for applications with huge datasets. However, the introduction of neural networks has helped to improve the aforementioned task considerably [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}. These deep networks consist of non-linear filters configured to extract key perceptual features within user-defined constraints from data.
